【深度学习】阿姆斯特丹大学：群体等变深度学习课程 by Erik Bekkers（2022年）
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阿姆斯特丹大学：群体等变深度学习课程 by Erik Bekkers
课程地址：https://uvadl2c.github.io/
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Lecture 1.1 Introduction
19:04
Lecture 1.2 Group theory (product, inverse, representations)
23:28
Lecture 1.3 Regular group convolutional neural networks
20:47
Lecture 1.4 Example
16:38
Lecture 1.5 A Brief History of G-CNNs
16:08
Lecture 1.6 Group theory (Homogeneousquotient spaces)
16:31
Lecture 1.7 Group convolutions are all you need
23:36
Lecture 2.1 Steerable kernelsbasis functions
22:28
Lecture 2.2 Revisiting Regular G-Convs with Steerable Kernels
15:20
Lecture 2.3 Group Theory (Irreducible representations, Fourier)
18:36
Lecture 2.4 Group Theory (Induced representation, feature fields)
08:57
Lecture 2.5 Steerable group convolutions
19:22
Lecture 2.6 Activation Functions for Steerable G-CNNs
11:34
Lecture 2.7 Derivation of Harmonic Networks from Regular G-Convs
20:15
Lecture 3.1 Motivation for SE(3) equivariant graph NNs
18:43
Lecture 3.2 Equivariant message passing as non-linear convolution
33:08
Lecture 3.3 Tensor products as conditional linear layers
12:35
Lecture 3.4 Group Theory (SO(3) irreps, Wigner-D, Clebsch-Gordan)
34:17
Lecture 3.5 3D Steerable graph convolutions (literature overview)
48:43
Lecture 3.6 Literature survey (Regular equivariant graph NNs)
43:56
Lecture 3.7 Gauge equivariant graph NNs
31:40
中分段要不要学临床？
中分段专业选择，张老师指点迷津
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